AI Assistant In Adobe Acrobat

AI Assistant In Adobe Acrobat — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  PARRY

  PARRY was an early example of a chatbot, implemented in 1972 by psychiatrist Kenneth Colby. == History == PARRY was written in 1972 by psychiatrist Kenneth Colby, then at Stanford University. While ELIZA was a simulation of a Rogerian therapist, PARRY attempted to simulate a person with paranoid schizophrenia. The program implemented a crude model of the behavior of a person with paranoid schizophrenia based on concepts, conceptualizations, and beliefs (judgements about conceptualizations: accept, reject, neutral). It also embodied a conversational strategy, and as such was a much more serious and advanced program than ELIZA. It was described as "ELIZA with attitude". PARRY was tested in the early 1970s using a variation of the Turing Test. A group of experienced psychiatrists analysed a combination of real patients and computers running PARRY through teleprinters. Another group of 33 psychiatrists were shown transcripts of the conversations. The two groups were then asked to identify which of the "patients" were human and which were computer programs. The psychiatrists were able to make the correct identification only 48 percent of the time — a figure consistent with random guessing. PARRY and ELIZA (also known as "the Doctor") interacted several times. The most famous of these exchanges occurred at the ICCC 1972, where PARRY and ELIZA were hooked up over ARPANET and responded to each other.

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  Computational theory of mind

  In philosophy of mind, the computational theory of mind (CTM), also known as computationalism, is a family of views that hold that the human mind is an information processing system and that cognition and consciousness together are a form of computation. It is closely related to functionalism, a broader theory that defines mental states by what they do rather than what they are made of. == History == Warren McCulloch and Walter Pitts (1943) were the first to suggest that neural activity is computational. They argued that neural computations explain cognition. A version of the theory was put forward by Peter Putnam and Robert W. Fuller in 1964. The theory was proposed in its modern form by Hilary Putnam in 1960 and 1961, aided by his then PhD student, philosopher and cognitive scientist Jerry Fodor, who continued the research as a post-doc in the 1960s, 1970s, and 1980s. It was later criticized by Putnam himself, John Searle, and others. == Classical computational theory of mind == The CTM holds that the human mind is a computational system that is realized (i.e., physically implemented) by neural activity in the brain. The theory can be elaborated in many ways and varies largely based on how the term computation is understood. In classical computational theory of mind (CCTM), computation is modeled in terms of Turing machines which manipulate symbols according to a rule, in combination with the internal state of the machine. A Turing machine is an abstract machine with unlimited time and storage. CCTM does not pretend that the mind looks like a Turing machine, but instead uses Turing machines as a formalism. Alan Turing argued that any symbolic algorithm executed by a human brain can in theory be replicated on a Turing machine. The critical aspect of such a computational model is that it allows to abstract away from particular physical details of the machine that is implementing the computation. For example, the appropriate computation could be implemented either by silicon chips or biological neural networks, so long as there is a series of outputs based on manipulations of inputs and internal states, performed according to a rule. Computational theories of mind are often said to require mental representation because 'input' into a computation comes in the form of symbols or representations of other objects. A computer cannot compute an actual object but must interpret and represent the object in some form and then compute the representation. Unlike CTM, the representational theory of mind shifts the focus to the symbols being manipulated. This approach better accounts for systematicity and productivity. In Fodor's view, the mind is a computational system that processes the language of thought. == Variants == Connectionist computationalism models the mind as a neural network. Steven Pinker and Alan Prince distinguish two types of connectionists: eliminative and implementationist. Eliminative connectionists generally reject classical CTMs and the idea of a structured, symbolic mind, whereas implementationists view neural networks and Turing machines as two potentially complementary levels of analysis. It is indeed possible in theory to implement a neural network in a Turing machine, or a Turing machine in a neural network. Building from the tradition of McCulloch and Pitts, the computational theory of cognition (CTC) states that neural computations explain cognition. The computational theory of mind asserts that not only cognition, but also phenomenal consciousness or qualia, are computational. That is to say, CTM entails CTC. While phenomenal consciousness could fulfill some other functional role, computational theory of cognition leaves open the possibility that some aspects of the mind could be non-computational. CTC, therefore, provides an important explanatory framework for understanding neural networks, while avoiding counter-arguments that center around phenomenal consciousness. == "Computer metaphor" == Computational theory of mind is not the same as the computer metaphor, comparing the mind to a modern-day digital computer. While the computer metaphor draws an analogy between the mind as software and the brain as hardware, CTM is the claim that the mind is literally a computational system. "Computational system" is not intended to mean a modern-day electronic computer. == Pancomputationalism == CTM raises a question that remains a subject of debate: what does it take for a physical system (such as a mind, or an artificial computer) to perform computations? A very straightforward account is based on a simple mapping between abstract mathematical computations and physical systems: a system performs computation C if and only if there is a mapping between a sequence of states individuated by C and a sequence of states individuated by a physical description of the system. Putnam (1988) and Searle (1992) argue that this simple mapping account (SMA) trivializes the empirical import of computational descriptions. As Putnam put it, "everything is a Probabilistic Automaton under some Description". Even rocks, walls, and buckets of water—contrary to appearances—are computing systems. Gualtiero Piccinini identifies different versions of pancomputationalism. Searle wrote:the wall behind my back is right now implementing the WordStar program, because there is some pattern of molecule movements that is isomorphic with the formal structure of WordStar. But if the wall is implementing WordStar, if it is a big enough wall it is implementing any program, including any program implemented in the brain.In response to the trivialization criticism, and to restrict SMA, philosophers of mind have offered different accounts of computational systems. These typically include causal account, semantic account, syntactic account, and mechanistic account. Instead of a semantic restriction, the syntactic account imposes a syntactic restriction. The mechanistic account was first introduced by Gualtiero Piccinini in 2007. == Criticism == A range of arguments have been proposed against physicalist conceptions used in computational theories of mind. An early, though indirect, criticism of the computational theory of mind comes from philosopher John Searle. In his thought experiment known as the Chinese room, Searle attempts to refute the claims that artificially intelligent agents can be said to have intentionality and understanding and that these systems, because they can be said to be minds themselves, are sufficient for the study of the human mind. Searle asks us to imagine that there is a man in a room with no way of communicating with anyone or anything outside of the room except for a piece of paper with symbols written on it that is passed under the door. With the paper, the man is to use a series of provided rule books to return paper containing different symbols. Unknown to the man in the room, these symbols are of a Chinese language, and this process generates a conversation that a Chinese speaker outside of the room can actually understand. Searle contends that the man in the room does not understand the Chinese conversation. This was originally written as a repudiation of the idea that computers work like minds. Objections like Searle's might be called insufficiency objections. They claim that computational theories of mind fail because computation is insufficient to account for some capacity of the mind. Arguments from qualia, such as Frank Jackson's knowledge argument, can be understood as objections to computational theories of mind in this way—though they take aim at physicalist conceptions of the mind in general, and not computational theories specifically. Objections have also been put forth that are directly tailored for computational theories of mind. Jerry Fodor himself argues that the mind is still a very long way from having been explained by the computational theory of mind. The main reason for this shortcoming is that most cognition is abductive and global, hence sensitive to all possibly relevant background beliefs to (dis)confirm a belief. This creates, among other problems, the frame problem for the computational theory, because the relevance of a belief is not one of its local, syntactic properties but context-dependent. Putnam himself (see in particular Representation and Reality and the first part of Renewing Philosophy) became a prominent critic of computationalism for a variety of reasons, including ones related to Searle's Chinese room arguments, questions of world-word reference relations, and thoughts about the mind-body problem. Regarding functionalism in particular, Putnam has claimed along lines similar to, but more general than Searle's arguments, that the question of whether the human mind can implement computational states is not relevant to the question of the nature of mind, because "every ordinary open system realizes every abstract finite automaton." Computationalists have responded by aiming to develop criteri

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  LightGBM

  LightGBM, short for Light Gradient-Boosting Machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft. It is based on decision tree algorithms and used for ranking, classification and other machine learning tasks. The development focus is on performance and scalability. == Overview == The LightGBM framework supports different algorithms including GBT, GBDT, GBRT, GBM, MART and RF. LightGBM has many of XGBoost's advantages, including sparse optimization, parallel training, multiple loss functions, regularization, bagging, and early stopping. A major difference between the two lies in the construction of trees. LightGBM does not grow a tree level-wise — row by row — as most other implementations do. Instead it grows trees leaf-wise. It will choose the leaf with max delta loss to grow. Besides, LightGBM does not use the widely used sorted-based decision tree learning algorithm, which searches the best split point on sorted feature values, as XGBoost or other implementations do. Instead, LightGBM implements a highly optimized histogram-based decision tree learning algorithm, which yields great advantages on both efficiency and memory consumption. The LightGBM algorithm utilizes two novel techniques called Gradient-Based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB) which allow the algorithm to run faster while maintaining a high level of accuracy. LightGBM works on Linux, Windows, and macOS and supports C++, Python, R, and C#. The source code is licensed under MIT License and available on GitHub. == Gradient-based one-side sampling == When using gradient descent, one thinks about the space of possible configurations of the model as a valley, in which the lowest part of the valley is the model which most closely fits the data. In this metaphor, one walks in different directions to learn how much lower the valley becomes. Typically, in gradient descent, one uses the whole set of data to calculate the valley's slopes. However, this commonly used method assumes that every data point is equally informative. By contrast, Gradient-Based One-Side Sampling (GOSS), a method first developed for gradient-boosted decision trees, does not rely on the assumption that all data are equally informative. Instead, it treats data points with smaller gradients (shallower slopes) as less informative by randomly dropping them. This is intended to filter out data which may have been influenced by noise, allowing the model to more accurately model the underlying relationships in the data. == Exclusive feature bundling == Exclusive feature bundling (EFB) is a near-lossless method to reduce the number of effective features. In a sparse feature space many features are nearly exclusive, implying they rarely take nonzero values simultaneously. One-hot encoded features are a perfect example of exclusive features. EFB bundles these features, reducing dimensionality to improve efficiency while maintaining a high level of accuracy. The bundle of exclusive features into a single feature is called an exclusive feature bundle.

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  Oracle Database

  Oracle AI Database (commonly referred to as Oracle Database, Oracle DBMS, Oracle Autonomous Database, or simply as Oracle) is a proprietary multi-model database management system produced and marketed by Oracle Corporation. It is a database commonly used for running online transaction processing (OLTP), data warehousing (DW) and mixed (OLTP & DW) database workloads. Oracle AI Database uses SQL for database updating and retrieval. Oracle Database runs on-premises, on Oracle engineered systems such as Oracle Exadata, on Oracle Cloud Infrastructure, and as a managed Autonomous Database service. It is also offered inside Microsoft Azure, Google Cloud, and Amazon Web Services data centers through Oracle's multicloud offerings. The current long-term support release, Oracle AI Database 26ai, became available in the cloud and on Oracle engineered systems in October 2025 and on-premises for Linux x86-64 in January 2026. == History == Larry Ellison and his two friends and former co-workers, Bob Miner and Ed Oates, started a consultancy called Software Development Laboratories (SDL) in 1977, later Oracle Corporation. SDL developed the original version of the Oracle software. The name Oracle comes from the code-name of a Central Intelligence Agency-funded project Ellison had worked on while formerly employed by Ampex; the CIA was Oracle's first customer, and allowed the company to use the code name for the new product. Ellison wanted his database to be compatible with IBM System R, but that company's Don Chamberlin declined to release its error codes. By 1985 Oracle advertised, however, that "Programs written for SQL/DS or DB2 will run unmodified" on the many non-IBM mainframes, minicomputers, and microcomputers its database supported "Because all versions of ORACLE are identical". Later releases introduced capabilities associated with successive eras of the product, including PL/SQL stored procedures and triggers in Oracle7 (1992), Real Application Clusters in Oracle9i (2001), grid infrastructure and automatic management in Oracle 10g (2003), the multitenant architecture and In-Memory Column Store in Oracle Database 12c (2013), and AI Vector Search and JSON Relational Duality in Oracle Database 23ai (2024). In October 2025 Oracle rebranded the 23ai line as Oracle AI Database 26ai. (see Release History) == Architecture == An Oracle Database system consists of an instance and a database. The instance is a set of memory structures and background processes; the database is the set of files that store data. The instance exists only in memory, and a single instance is associated with one multitenant container database. The principal memory structures are the System Global Area, which is shared, and the Program Global Areas, which are private to individual processes. The shared pool, database buffer cache, and redo log buffer are components of the System Global Area, and the optional In-Memory Column Store also resides there. Background processes operate on the database files and use these memory structures; they include the database writer, the log writer, the checkpoint process, and the system and process monitor processes. Server processes handle connections from client programs and run their SQL statements. Storage is organized logically and physically. Logically, data is held in tablespaces composed of segments, extents, and data blocks. Physically, the database comprises datafiles, control files, and online redo log files, with archived redo logs supporting media recovery. == High Availability and Scalability == Oracle Database includes several technologies for high availability, disaster recovery, and scale. Oracle Real Application Clusters allows multiple instances on separate servers to access one shared database concurrently; it was introduced with Oracle9i in 2001. Oracle Data Guard maintains standby databases synchronized with a primary database, and Active Data Guard additionally allows read-only workloads on a standby while it applies changes. Oracle GoldenGate performs logical replication and data integration across heterogeneous systems. Native sharding, introduced in Oracle Database 12c Release 2, distributes one logical database across independent shards. Oracle Exadata is an engineered system that pairs database servers with storage servers and offloads operations such as filtering to the storage tier; it is available on-premises, in Oracle Cloud Infrastructure, and through Cloud@Customer. == Notable Features == AI Vector Search adds a vector data type, vector indexes, and vector distance operators to the database. These allow similarity search over machine-learning embeddings to be expressed in SQL and combined with queries over relational, JSON, spatial, and graph data. It became generally available in Oracle Database 23ai. JSON Relational Duality exposes the same data both as relational tables and as JSON documents through duality views, so that an application can read and write either representation of the data. It became generally available in Oracle Database 23ai. In-Memory Column Store maintains a column-oriented copy of selected tables in memory in addition to the row-oriented format, and the optimizer can use the columnar copy for analytic queries. It was introduced in Oracle Database 12c Release 1.Partitioning divides large tables and indexes into independently managed pieces. Advanced Compression and Hybrid Columnar Compression are compression features for transactional and warehouse data respectively. == Data Types == Oracle AI Database supports a variety of data types and data models within a single system. These include traditional relational data types as well as semi-structured, unstructured, and specialized data formats, enabling different types of data to be stored and queried together. == Releases and versions == Oracle products follow a custom release-numbering and -naming convention. The "ai" in the current release, Oracle AI Database 26ai, stands for "Artificial Intelligence". Previous releases (e.g. Oracle Database 19c, 10g, and Oracle9i Database) have used suffixes of "c", "g", and "i" which stand for "Cloud", "Grid", and "Internet" respectively. Prior to the release of Oracle8i Database, no suffixes featured in Oracle AI Database naming conventions. There was no v1 of Oracle AI Database, as Ellison "knew no one would want to buy version 1". For some database releases, Oracle also provides an Express Edition (XE) that is free to use. Oracle AI Database release numbering has used the following codes: The Introduction to Oracle AI Database includes a brief history on some of the key innovations introduced with each major release of Oracle AI Database. See My Oracle Support (MOS) note Release Schedule of Current Database Releases (Doc ID 742060.1) for the current Oracle AI Database releases and their patching end dates. == Patch updates and security alerts == Prior to Oracle Database 18c, Oracle Corporation released Critical Patch Updates (CPUs) and Security Patch Updates (SPUs) and Security Alerts to close security vulnerabilities. These releases are issued quarterly; some of these releases have updates issued prior to the next quarterly release. Starting with Oracle Database 18c, Oracle Corporation releases Release Updates (RUs) and Release Update Revisions (RURs). RUs usually contain security, regression (bug), optimizer, and functional fixes which may include feature extensions as well. RURs include all fixes from their corresponding RU but only add new security and regression fixes. However, no new optimizer or functional fixes are included. == Competition == In the market for relational databases, Oracle AI Database competes against commercial products such as IBM Db2 and Microsoft SQL Server. Oracle and IBM tend to battle for the mid-range database market on Unix and Linux platforms, while Microsoft dominates the mid-range database market on Microsoft Windows platforms. However, since they share many of the same customers, Oracle and IBM tend to support each other's products in many middleware and application categories (for example: WebSphere, PeopleSoft, and Siebel Systems CRM), and IBM's hardware divisions work closely with Oracle on performance-optimizing server-technologies (for example, Linux on IBM Z). Niche commercial competitors include Teradata (in data warehousing and business intelligence), Software AG's ADABAS, Sybase, and IBM's Informix, among many others. In the cloud, Oracle AI Database competes against the database services of AWS, Microsoft Azure, and Google Cloud Platform. Increasingly, the Oracle AI Database products compete against open-source software relational and non-relational database systems such as PostgreSQL, MongoDB, Couchbase, Neo4j, ArangoDB and others. Oracle acquired Innobase, supplier of the InnoDB codebase to MySQL, in part to compete better against open source alternatives, and acquired Sun Microsystems, owner of MySQL, in 2010. Database products licensed as open

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  Underwater computer vision

  Underwater computer vision is a subfield of computer vision. In recent years, with the development of underwater vehicles ( ROV, AUV, gliders), the need to be able to record and process huge amounts of information has become increasingly important. Applications range from inspection of underwater structures for the offshore industry to the identification and counting of fishes for biological research. However, no matter how big the impact of this technology can be to industry and research, it still is in a very early stage of development compared to traditional computer vision. One reason for this is that, the moment the camera goes into the water, a whole new set of challenges appear. On one hand, cameras have to be made waterproof, marine corrosion deteriorates materials quickly and access and modifications to experimental setups are costly, both in time and resources. On the other hand, the physical properties of the water make light behave differently, changing the appearance of a same object with variations of depth, organic material, currents, temperature etc. == Applications == Seafloor survey Vehicle navigation and positioning Biological monitoring {possibly aquatic biomonitoring) Video mosaics as visual navigation maps Submarine pipeline inspection Wreckage visualization Maintenance of underwater structures Drowning detection systems == Medium differences == === Illumination === In air, light comes from the whole hemisphere on cloudy days, and is dominated by the sun. In water direct lighting comes from a cone about 96° wide above the scene. This phenomenon is called Snell's window. Artificial lighting can be used where natural light levels are insufficient and where the light path is too long to produce acceptable colour, as the loss of colour is a function of the total distance through water from the source to the camera lens port. === Light attenuation === Unlike air, water attenuates light exponentially. This results in hazy images with very low contrast. The main reasons for light attenuation are light absorption (where energy is removed from the light) and light scattering, by which the direction of light is changed. Light scattering can further be divided into forward scattering, which results in an increased blurriness and backward scattering that limits the contrast and is responsible for the characteristic veil of underwater images. Both scattering and attenuation are heavily influenced by the amount of organic matter dissolved or suspended in the water. Light attenuation in water is also a function of the wavelength. This means that different colours are attenuated at different rates, leading to colour degradation.with depth and distance. Red and orange light are attenuated faster, followed by yellows and greens. Blue is the least attenuated visible wavelength. === Artificial lighting === == Challenges == In high level computer vision, human structures are frequently used as image features for image matching in different applications. However, the sea bottom lacks such features, making it hard to find correspondences in two images. In order to be able to use a camera in the water, a watertight housing is required. However, refraction will happen at the water-glass and glass-air interface due to differences in density of the materials. This has the effect of introducing a non-linear image deformation. The motion of the vehicle presents another special challenge. Underwater vehicles are constantly moving due to currents and other phenomena. This introduces another uncertainty to algorithms, where small motions may appear in all directions. This can be specially important for video tracking. In order to reduce this problem image stabilization algorithms may be applied. == Relevant technology == === Image restoration === Image restoration< techniques are intended to model the degradation process and then invert it, obtaining the new image after solving. It is generally a complex approach that requires plenty of parameters that vary a lot between different water conditions. === Image enhancement === Image enhancement only tries to provide a visually more appealing image without taking the physical image formation process into account. These methods are usually simpler and less computational intensive. === Color correction === Various algorithms exist that perform automatic color correction. The UCM (Unsupervised Color Correction Method), for example, does this in the following steps: It firstly reduces the color cast by equalizing the color values. Then it enhances contrast by stretching the red histogram towards the maximum and finally saturation and intensity components are optimized. == Underwater stereo vision == It is usually assumed that stereo cameras have been calibrated previously, geometrically and radiometrically. This leads to the assumption that corresponding pixels should have the same color. However this can not be guaranteed in an underwater scene, because of dispersion and backscatter. However, it is possible to digitally model this phenomenon and create a virtual image with those effects removed == Other application fields == Imaging sonars have become more and more accessible and gained resolution, delivering better images. Sidescan sonars are used to produce complete maps of regions of the sea floor stitching together sequences of sonar images. However, sonar images often lack proper contrast and are degraded by artefacts and distortions due to noise, attitude changes of the AUV/ROV carrying the sonar or non uniform beam patterns. Another common problem with sonar computer vision is the comparatively low frame rate of sonar images.

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  Parallel terraced scan

  The parallel terraced scan is a multi-agent based search technique that is basic to cognitive architectures, such as Copycat, Letter-string, the Examiner, Tabletop, and others. It was developed by John Rehling and Douglas Hofstadter at the Center for Research on Concepts and Cognition at Indiana University, Bloomington. The parallel terraced scan builds on the concepts of the workspace, coderack, conceptual memory, and temperature. According to Hofstadter the parallel and random nature of the processing captures aspects of human cognition.

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  Komodo (chess)

  Komodo and Dragon by Komodo Chess (also known as Dragon or Komodo Dragon) are UCI chess engines developed by Komodo Chess, which is a part of Chess.com. The engines were originally authored by Don Dailey and GM Larry Kaufman. Dragon is a commercial chess engine, but Komodo is free for non-commercial use. Dragon is consistently ranked near the top of most major chess engine rating lists, along with Stockfish and Leela Chess Zero. == History == === Komodo === Komodo was derived from Don Dailey's former engine Doch in January 2010. The first multiprocessor version of Komodo was released in June 2013 as Komodo 5.1 MP. This version was a major rewrite and a port of Komodo to C++11. A single-processor version of Komodo (which won the CCT15 tournament in February earlier that year) was released as a stand-alone product shortly before the 5.1 MP release. This version, named Komodo CCT, was still based on the older C code, and was approximately 30 Elo stronger than the 5.1 MP version, as the latter was still undergoing massive code-cleanup work. With the release of Komodo 6 on October 4, 2013, Don Dailey announced that he was suffering from an acute form of leukaemia, and would no longer contribute to the future development of Komodo. On October 8, Don made an announcement on the Talkchess forum that Mark Lefler would be joining the Komodo team and would continue its development. Komodo TCEC was released on December 4, 2013. This was the same version that had won TCEC Season 5, and was the last with input from Don Dailey, to whom it was dedicated. Komodo 7 was released on May 21, 2014, adding Syzygy tablebase support. On May 24, 2018, Chess.com announced that it has acquired Komodo and that the Komodo team have joined Chess.com. The Komodo team is now called Komodo Chess. On December 17, 2018, Komodo Chess released Komodo 12.3 MCTS, a version of the Komodo 12.3 engine that uses Monte Carlo tree search instead of alpha–beta pruning/minimax. The last version, Komodo 14.3, was released on October 4, 2023. === Dragon === On November 9, 2020, Komodo Chess released Dragon by Komodo Chess 1.0, which features the use of efficiently updatable neural networks in its evaluation function. Dragon is derived from Komodo in the same way that Komodo was derived from Doch. Dragon is also called Komodo Dragon in certain tournaments such as the Top Chess Engine Championship and the World Computer Chess Championship (WCCC) but not in the Chess.com Computer Chess Championship (CCC). A Chess.com staff member named Dmitry Pervov joined the Dragon development team to write the NNUE code for Dragon, and Dietrich Kappe joined the Dragon development team to help Larry Kaufman and Mark Lefter train Dragon's neural networks. On March 17, 2023, Larry Kaufman announced that he and Mark Lefter have stepped down from Dragon development and from ownership of Komodo Chess, and that Chess.com have taken full control of Komodo Chess. As of March 17, 2023, Dietrich Kappe is the only person responsible for the development of Dragon, but Chess.com are looking for more programmers to help with Dragon development. The final version, Dragon 3.3, was released on October 4, 2023. == Competition results == === Komodo === Komodo has played in the ICT 2010 in Leiden, and further in the CCT12 and CCT14. Komodo had its first tournament success in 1999, when it won the CCT15 with a score of 6½/7. Komodo won both the World Computer Chess Championship and World Computer Software Championship in 2016. Komodo once again won the World Computer Chess Championship and World Blitz in 2017. In TCEC competition, Komodo was historically one of the strongest engines. In Season 4, it lost only eight out of its 53 games and managed to reach Stage 4 (Quarterfinals), against very strong competition which were running on eight cores (Komodo was running on a single processor). The next season, Komodo won the superfinal against Stockfish. The two engines jockeyed for the championship over the next few seasons: Stockfish won in Season 6, while Komodo won Seasons 7 and 8. Komodo failed to make the superfinal in Season 9, losing out to Houdini; but after Houdini was later disqualified for containing code plagiarized from Stockfish, Komodo was promoted to the runner-up. Komodo retrospectively won Season 10 in the same way. Starting from Season 11 however, Stockfish improved at a rate that left its rivals behind, crushing Komodo in Season 12 and 13. The advent of the neural network engine Leela Chess Zero meant Komodo has largely failed to qualify for the superfinal since, with a single exception in Season 22, when it lost to Stockfish. Although Komodo has not qualified for the superfinal, it has cemented itself as the third-strongest engine in the competition, finishing in that position for five of the last six seasons. ==== Chess.com Computer Chess Championship ==== === Dragon === ==== Chess.com Computer Chess Championship ==== ==== Top Chess Engine Championship ==== == Notable games == Komodo vs Hannibal, nTCEC - Stage 2b - Season 1, Round 4.1, ECO: A10, 1–0 Archived 2016-03-04 at the Wayback Machine Komodo sacrifices an exchange for positional gain. Gull vs Komodo, nTCEC - Stage 3 - Season 2, Round 2.2, ECO: E10, 0–1 Archived March 4, 2016, at the Wayback Machine Archived 2016-03-04 at the Wayback Machine

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  Information Coding Classification

  The Information Coding Classification (ICC) is a classification system covering almost all extant 6500 knowledge fields (knowledge domains). Its conceptualization goes beyond the scope of the well known library classification systems, such as Dewey Decimal Classification (DDC), Universal Decimal Classification (UDC), and Library of Congress Classification (LCC), by extending also to knowledge systems that so far have not afforded to classify literature. ICC actually presents a flexible universal ordering system for both literature and other kinds of information, set out as knowledge fields. From a methodological point of view, ICC differs from the above-mentioned systems along the following three lines: Its main classes are not based on disciplines but on nine live stages of development, so-called ontical levels. It breaks them roughly down into hierarchical steps by further nine categories which makes decimal number coding possible. The contents of a knowledge field is earmarked via a digital position scheme, which makes the first hierarchical step refer to the nine ontical levels (object areas as subject categories), and the second hierarchical step refer to nine functionally ordered form categories. Respective knowledge fields permit to step down by the same principle to a third and forth level, and even further to a fifth and sixth level. Finally, knowledge field subdivisions will have to conform to said digital position scheme. Hence, for a given knowledge field identical codes will mark identical categories under respective numbers of the coding system. This mnemotechnical aspect of the system helps memorizing and straightaway retrieving the whereabouts of respective interdisciplinary and transdisciplinary fields. The first two hierarchical levels may be regarded as a top- or upper ontology for ontologies and other applications. The terms of the first three hierarchical levels were set out in German and English in Wissensorganisation. Entwicklung, Aufgabe, Anwendung, Zukunft, on pp. 82 to 100. It was published in 2014 and available so far only in German. In the meantime, also the French terms of the knowledge fields have been collected. Competence for maintenance and further development rests with the German Chapter of the International Society for Knowledge Organization (ISKO) e.V. == Historical development == At the end of 1970, Prof. Alwin Diemer, Univ.of Düsseldorf proposed to Ingetraut Dahlberg to undertake a philosophical dissertation on The universal classification system of knowledge, its ontological, epistemological, and information theoretical foundations. Diemer had in mind an innovating ontological approach for such a system based on the whole spectrum of kinds of being and complying with epistemological requirements. The third requirement had already been taken up somehow in the Indian Colon Classification, yet it still called for explanations and additions. In 1974, the dissertation was published in German entitled Grundlagen universaler Wissensordnung. It started with conceptual clarifications, and why and how the term „universal“ was linked to knowledge, including knowledge fields, such as commodity science, artefacts, statistics, patents, standardization, communication, utility services et al. In chapter 3, six universal classification systems (DDC, UDC, LCC, BC, CC and BBK) were presented, analyzed and compared. While preparing the dissertation, Dahlberg started with elaborating the new universal system by first gleaning a lot of extant designations of knowledge fields from whatever available reference works. This was funded by the German Documentation Society (DGD) (1971-2) under the title of Order system of knowledge fields. In addition, the syllabuses of German universities and polytechniques were explored for relevant terms and documented (1975). Thereafter, it seemed necessary to add definitions from special dictionaries and encyclopediae; it soon appeared that the 12.500 terms included numerous synonyms, so that the whole collection boiled down to about 6.500 concept designations (Project Logstruktur, supported by the German Science Foundation (DFG) 1976-78). The outcome of this work was the formulation of 30 theses which ended up in 12 principles for the new system, published 40 years later under. These principles refer not only to theoretical foundations but also to structure and other organizational aspects of the whole array of knowledge fields. In 1974, the digital position scheme for field subdivision had already been developed to allow for classifying classification literature in the bibliographical section of the first issue of the Journal International Classification. In 1977, the entire ICC was ready for presentation at a seminar in Bangalore, India. A publication of the first three hierarchical levels appeared however only in 1982. It was applied to the bibliography of classification systems and thesauri in vol.1 of the International Classification and Indexing Bibliography; it has been updated. == Governing principles == These were published in full length in the book Wissensorganisation. Entwicklung, Aufgabe, Anwendung, Zukunft and the article Information Coding Classification. Geschichtliches, Prinzipien, Inhaltliches, hence it suffices to just mention their topics with some necessary additions. Principle 1: Concept theoretical approaches. Concepts are the contents of ICC, they are understood as being units of knowledge. The „birth“ of a concept. Where do the characteristics, the knowledge elements come from? How do conceptual relations arise? Principle 2: The four kinds of concept relations and their applications. Principle 3: Decimal numbers form the ICC codes as its universal language. Principle 4: The nine ontical levels of ICC. They were grouped under three captions: Prolegomena (1-3), life sciences (4-6) and human output (7-9): Structure and form Matter and energy Cosmos and earth Biosphere Anthroposphere Sociosphere Material products (economics and technology) Intellectual products (knowledge and information) Spiritual products (products of mind and culture) Principle 5: Knowledge fields are structured by categories, based on the Aristotelian form-categories, under a digital position scheme, a kind of scaling rule for subdividing a given field as follows: General area: problems, theories, principles (axiom and structure) Object area: objects, kinds, parts, properties of objects Activity area: methods, processes, activities Field properties or first characterization Persons or secondary characterization Societies or tertiary characterization Influences from outside Applications of the field to other fields Field information and synthesizing tasks The digital position scheme, called Systematifier, has also been used for structuring the entire system via the categories figuring on the upper zero level. An example of its application is the structure of the classification system for knowledge organization literature Gliederung der Klassifikationsliteratur. (A simplified version with an additional introduction is given in, p. 71) Principle 6: The ontical levels outlined under principle 4 conform to the „integrative level theory“ which means that every level is integrated in the following one. In addition, each knowledge area presumes the following one. Principle 7: The combination potential of knowledge fields (interdisciplinarity and transdisciplinarity)is determined by the digital position scheme. (Examples are given in, p. 103-4) Principle 8: The categories of the zero-level are general concepts, their possible subdivisions could once be used for classificatory statements. (These subdivisions still need elaboration) Principle 9 and 10: These relate to the combination potential of classificatory statements with space and time concepts. (Still to be elaborated) Principle 11: The system's mnemotechnical aspect relies on the fixed system position codes and on the 3x3 form- and subject-categories. Principle 12: The combination potential of system position 1, 8 and 9 make ICC to a self-networking system which complies with the present scientific development. == In matrix form == The first two levels of ICC can be represented by following matrix. The first hierarchical level of the 9 subject categories results from the first vertical array under codes 1-9. The second hierarchical level of subject categories is structured by the 9 functionally ordered form categories, listed in the first horizontal line under codes 01-09. Some exceptions are mentioned in principle 7. == Research == === Exploration of automatic classification === For classifying web documents as conceived by Jens Hartmann, University of Karlsruhe, Prof.Walter Koch, University of Graz, has explored in his Institute for Applied Information Technology Research Society (AIT) the application of ICC to automatically classifying metadata of some 350.000 documents. This was facilitated by data generated within the framework of an E

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  Objective vision

  Objective Vision (Object Oriented Visionary) is a project mainly aimed at real-time computer vision and simulation vision of living creatures. it has three sections containing an open-source library of programming functions for using inside the projects, Virtual laboratory for scholars to check the application of functions directly and by command-line code for external and instant access, and the research section consists of paperwork and libraries to expand the scientific prove of works. == Background == The process has been used in the OVC libraries is as same as what's happening when living see a picture, and it's designed to give the researchers to experience the brain's visual cortex most close simulation for picture perception. The OVC was designed to work as a simulated visual cortex that has a critical job in processing and classify the objects to make it easier to work with pictures and graphical perception and processing. The human brain is much more aware of how it solves complex problems such as playing chess or solving algebra equations, which is why computer programmers have had so much success building machines that emulate this type of activity. but when the whole process is still a riddle that how the entities visionary system works. The project was simulated the visionary system by how it starts to convert the signals to image(actually the edges and colors) and then recognizing the shapes to find a relation between brain's information and image. The Objective Visionary system actually is concentrating on the separable sections, this separation gives the application visionary system the excellence processing result, because with this method the system do not waste much time on processing non significant sections and signals. this operation in the Objective Vision project called objective processing and because the O.V. mission is focused on human visionary simulation, so the developer refers with Objective Vision. == History == Objective-Vision is a Human (Natural) Visionary simulation Project developed by Michael Bidollahkhany. Following an explosion of interest during the 21st century were characterized by the maturing of the field and the significant growth of active applications; simulation of visionary systems, visionary based autonomous vehicle guidance, medical imaging (2D and 3D) and automatic surveillance are the most rapidly developing areas. This progress can be seen in an increasing number of software and hardware products on the market, as well as in a number of digital image processing software and APIs and also machine vision courses offered at universities worldwide. Therefore, the OVC project has been released as a research software project in 2016. One of important parts of this project was O.V.C. (Objective Vision Class library), that was designed to able companies and scientists to use the brain's most likely functionalities as visionary libraries to simplify and accelerate the image processing algorithms developments. The project started under MIT copyright license, but since 2018 the project continued as classified based on sponsors opinion. == The Algorithm == As developers claimed the algorithm used in the class library and developer's kit of project has been developed based on natural visionary system, and the functionalities containing image processing, optimization and labeling etc. are mostly upgraded and near techniques. Suppose that we've a picture of a jungle, or somewhere else, with this library developer will be able to manipulate not only the pixel of images for data extraction, but automatically based on which algorithm is used and image quality, he can manipulate directly a list of objects, same pixels and every data project needs to have, said the developer in his lecture answering how the algorithm works. === Viewpoint === For long times digital image processing and storing, was actually by processing just pixels; this Project tries to present a new kind of image processing and even storing, "objective vision" or "object-oriented visionary" is called. This project officially launched in May 2016, with the aim of making more adaptation between Computer Vision (Include Visionary, Digital image processing, discernment and even Perception) and Human Visual System; about development of the project: "...so we decided to research on Human Vision System, besides we worked on Artificial Retinal image processing and new visionary optimization unit(Presented at Istanbul Technical University Conference(Turkey 2015-2016)) and grew our research to Visionary CORTEX of Brain", Michael Bidollahkhany said. == Applications == The OVC application areas include: 2D and 3D feature toolkits Egomotion estimation Human–computer interaction (HCI) Mobile robotics Motion understanding Object identification Segmentation and recognition Stereopsis stereo vision: depth perception from two cameras Structure from motion (SFM) Motion tracking == Programming language == In first initial release of Objective Visionary Project the algorithm has been written in C++ and C#, and the virtual laboratory has been developed in C# and Delphi. Based on developers last lecture since the second release the complete algorithm has been re-written in C# based on .Net Core 1.0 to make it easier to work on different operating systems.

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  RevoScaleR

  RevoScaleR is a machine learning package in R created by Microsoft. It is available as part of Machine Learning Server, Microsoft R Client, and Machine Learning Services in Microsoft SQL Server 2016. The package contains functions for creating linear model, logistic regression, random forest, decision tree and boosted decision tree, and K-means, in addition to some summary functions for inspecting and visualizing data. It has a Python package counterpart called revoscalepy. Another closely related package is MicrosoftML, which contains machine learning algorithms that RevoScaleR does not have, such as neural network and SVM. In June 2021, Microsoft announced to open source the RevoScaleR and revoscalepy packages, making them freely available under the MIT License. == Concepts == Many R packages are designed to analyze data that can fit in the memory of the machine and usually do not make use of parallel processing. RevoScaleR was designed to address these limitations. The functions in RevoScaleR orientate around three main abstraction concepts that users can specify to process large amount of data that might not fit in memory and exploit parallel resources to speed up the analysis. === Compute Contexts === A compute context refers to the location where the computation on the data happens. It could be "local" (on the client machine) or "remote" (on a data platform such as a SQL server, or Spark). Pushing the computation to a remote server allows people to take advantage of the greater compute resources that a remote machine may have. If the data being analyzed reside on the same machine, using a remote compute context also removes the need to pull data across the network onto the client machine. === Data source === Data source defines where the data comes from. There are various data sources available in RevoScaleR, such as text data, Xdf data, in-SQL data, and a spark dataframe. People can wrap their data in a data source object and use that as run analytics in different compute context. Different data sources are available in different compute context. For example, if the compute context is set to SQL server, then the only data source one can use would be an in-SQL data source. === Analytics === Analytic functions in RevoScaleR takes in data source object, a compute context, and the other parameters needed to build the specific model, such as formula for the logistic regression or the number of trees in a decision tree. In addition to those parameters, one can also specify the level of parallelism, such as the size of the data chunk for each process or number of processes to build the model. However, parallelism is only available in non-express edition. == Limitations == The package is mostly meant to be used with a SQL server or other remote machines. To fully leverage the abstractions it uses to process a large dataset, one needs a remote server and non-Express free edition of the package. It cannot be easily installed such as by running "install.packages("RevoScaleR")" like most open source R packages. It's available only through Microsoft R Client, a distribution of R for data science, or Microsoft Machine Learning Server (stand-alone with no SQL server attached), or Microsoft Machine Learning Services (a SQL server services). However, one can still use the analytics functions in an Express, free version of the package.

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  Turing test

  The Turing test, originally called the imitation game by Alan Turing in 1949, is a test of a machine's ability to exhibit intelligent behaviour equivalent to that of a human. In the test, a human evaluator judges a text transcript of a natural-language conversation between a human and a machine. The evaluator tries to identify the machine, and the machine passes if the evaluator cannot reliably tell them apart. The results would not depend on the machine's ability to answer questions correctly, only on how closely its answers resembled those of a human. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic). The test was introduced by Turing in his 1950 paper "Computing Machinery and Intelligence" while working at the University of Manchester. It opens with the words: "I propose to consider the question, 'Can machines think?'." Because "thinking" is difficult to define, Turing chooses to "replace the question by another, which is closely related to it and is expressed in relatively unambiguous words". Turing describes the new form of the problem in terms of a three-person party game called the "imitation game", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: "Are there imaginable digital computers which would do well in the imitation game?" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against the major objections to the proposition that "machines can think". Since Turing introduced his test, it has been highly influential in the philosophy of artificial intelligence, resulting in substantial discussion and controversy, as well as criticism from philosophers like John Searle, who argue against the test's ability to detect consciousness. == History == === Philosophical background === The question of whether it is possible for machines to think has a long history, which is firmly entrenched in the distinction between dualist and materialist views of the mind. René Descartes prefigures aspects of the Turing test in his 1637 Discourse on the Method when he writes: [H]ow many different automata or moving machines could be made by the industry of man ... For we can easily understand a machine's being constituted so that it can utter words, and even emit some responses to action on it of a corporeal kind, which brings about a change in its organs; for instance, if touched in a particular part it may ask what we wish to say to it; if in another part it may exclaim that it is being hurt, and so on. But it never happens that it arranges its speech in various ways, in order to reply appropriately to everything that may be said in its presence, as even the lowest type of man can do. Here Descartes notes that automata are capable of responding to human interactions but argues that such automata cannot respond appropriately to things said in their presence in the way that any human can. Descartes therefore prefigures the Turing test by defining the insufficiency of appropriate linguistic response as that which separates the human from the automaton. Descartes fails to consider the possibility that future automata might be able to overcome such insufficiency, and so does not propose the Turing test as such, even if he prefigures its conceptual framework and criterion. Denis Diderot formulates in his 1746 book Pensées philosophiques a Turing-test criterion, though with the important implicit limiting assumption maintained, of the participants being natural living beings, rather than considering created artifacts: If they find a parrot who could answer to everything, I would claim it to be an intelligent being without hesitation. This does not mean he agrees with this, but that it was already a common argument of materialists at that time. According to dualism, the mind is non-physical (or, at the very least, has non-physical properties) and, therefore, cannot be explained in purely physical terms. According to materialism, the mind can be explained physically, which leaves open the possibility of minds that are produced artificially. In 1936, philosopher Alfred Ayer considered the standard philosophical question of other minds: how do we know that other people have the same conscious experiences that we do? In his book, Language, Truth and Logic, Ayer suggested a protocol to distinguish between a conscious man and an unconscious machine: "The only ground I can have for asserting that an object which appears to be conscious is not really a conscious being, but only a dummy or a machine, is that it fails to satisfy one of the empirical tests by which the presence or absence of consciousness is determined". (This suggestion is very similar to the Turing test, but it is not certain that Ayer's popular philosophical classic was familiar to Turing.) In other words, a thing is not conscious if it fails the consciousness test. === Cultural background === A rudimentary idea of the Turing test appears in the 1726 novel Gulliver's Travels by Jonathan Swift. When Gulliver is brought before the king of Brobdingnag, the king thinks at first that Gulliver might be a "a piece of clock-work (which is in that country arrived to a very great perfection) contrived by some ingenious artist". Even when he hears Gulliver speaking, the king still doubts whether Gulliver was taught "a set of words" to make him "sell at a better price". Gulliver tells that only after "he put several other questions to me, and still received rational answers" the king became satisfied that Gulliver was not a machine. Tests where a human judges whether a computer or an alien is intelligent were an established convention in science fiction by the 1940s, and it is likely that Turing would have been aware of these. Stanley G. Weinbaum's "A Martian Odyssey" (1934) provides an example of how nuanced such tests could be. Earlier examples of machines or automatons attempting to pass as human include the Ancient Greek myth of Pygmalion who creates a sculpture of a woman that is animated by Aphrodite, Carlo Collodi's novel The Adventures of Pinocchio, about a puppet who wants to become a real boy, and E. T. A. Hoffmann's 1816 story "The Sandman," where the protagonist falls in love with an automaton. In all these examples, people are fooled by artificial beings that—up to a point—pass as human. === Alan Turing and the imitation game === Researchers in the United Kingdom had been exploring "machine intelligence" for up to ten years prior to the founding of the field of artificial intelligence (AI) research in 1956. It was a common topic among the members of the Ratio Club, an informal group of British cybernetics and electronics researchers that included Alan Turing. Turing, in particular, had been running the notion of machine intelligence since at least 1941 and one of the earliest-known mentions of "computer intelligence" was made by him in 1947. In Turing's report, "Intelligent Machinery," he investigated "the question of whether or not it is possible for machinery to show intelligent behaviour" and, as part of that investigation, proposed what may be considered the forerunner to his later tests: It is not difficult to devise a paper machine which will play a not very bad game of chess. Now get three men A, B and C as subjects for the experiment. A and C are to be rather poor chess players, B is the operator who works the paper machine. ... Two rooms are used with some arrangement for communicating moves, and a game is played between C and either A or the paper machine. C may find it quite difficult to tell which he is playing. "Computing Machinery and Intelligence" (1950) was the first published paper by Turing to focus exclusively on machine intelligence. Turing begins the 1950 paper with the claim, "I propose to consider the question 'Can machines think?'" As he highlights, the traditional approach to such a question is to start with definitions, defining both the terms "machine" and "think". Turing chooses not to do so; instead, he replaces the question with a new one, "which is closely related to it and is expressed in relatively unambiguous words". In essence he proposes to change the question from "Can machines think?" to "Can machines do what we (as thinking entities) can do?" The advantage of the new question, Turing argues, is that it draws "a fairly sharp line between the physical and intellectual capacities of a man". To demonstrate this approach Turing proposes a test inspired by a party game, known as the "imitation game", in which a man and a woman go into separate rooms and guests try to tell them apart by writing a series of questions and reading the typewritten answers sent back. In this game, both the man and the woman aim to convince the guests that they ar

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  OpenVX

  OpenVX is an open, royalty-free standard for cross-platform acceleration of computer vision applications. It is designed by the Khronos Group to facilitate portable, optimized and power-efficient processing of methods for vision algorithms. This is aimed for embedded and real-time programs within computer vision and related scenarios. It uses a connected graph representation of operations. == Overview == OpenVX specifies a higher level of abstraction for programming computer vision use cases than compute frameworks such as OpenCL. The high level makes the programming easy and the underlying execution will be efficient on different computing architectures. This is done while having a consistent and portable vision acceleration API. OpenVX is based on a connected graph of vision nodes that can execute the preferred chain of operations. It uses an opaque memory model, allowing to move image data between the host (CPU) memory and accelerator, such as GPU memory. As a result, the OpenVX implementation can optimize the execution through various techniques, such as acceleration on various processing units or dedicated hardware. This architecture facilitates applications programmed in OpenVX on different systems with different power and performance, including battery-sensitive, vision-enabled, wearable displays. OpenVX is complementary to the open source vision library OpenCV. OpenVX in some applications offers a better optimized graph management than OpenCV. == History == OpenVX 1.0 specification was released in October 2014. OpenVX sample implementation was released in December 2014. OpenVX 1.1 specification was released on May 2, 2016. OpenVX 1.2 was released on May 1, 2017. Updated OpenVX adopters program and OpenVX 1.2 conformance test suite was released on November 21, 2017. OpenVX 1.2.1 was released on November 27, 2018. OpenVX 1.3 was released on October 22, 2019. == Implementations, frameworks and libraries == AMD MIVisionX Archived 2019-08-05 at the Wayback Machine - for AMD's CPUs and GPUs. Cadence - for Cadence Design Systems's Tensilica Vision DSPs. Imagination - for Imagination Technologies's PowerVR GPUs Synopsys - for Synopsys' DesignWare EV Vision Processors Texas Instruments’ OpenVX (TIOVX) - for Texas Instruments’ Jacinto™ ADAS SoCs. NVIDIA VisionWorks - for CUDA-capable Nvidia GPUs and SoCs. OpenVINO - for Intel's CPUs, GPUs, VPUs, and FPGAs.

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  Image texture

  An image texture is the small-scale structure perceived on an image, based on the spatial arrangement of color or intensities. It can be quantified by a set of metrics calculated in image processing. Image texture metrics give us information about the whole image or selected regions. Image textures can be artificially created or found in natural scenes captured in an image. Image textures are one way that can be used to help in segmentation or classification of images. For more accurate segmentation the most useful features are spatial frequency and an average grey level. To analyze an image texture in computer graphics, there are two ways to approach the issue: structured approach and statistical approach. == Structured approach == A structured approach sees an image texture as a set of primitive texels in some regular or repeated pattern. This works well when analyzing artificial textures. To obtain a structured description a characterization of the spatial relationship of the texels is gathered by using Voronoi tessellation of the texels. == Statistical approach == A statistical approach sees an image texture as a quantitative measure of the arrangement of intensities in a region. In general this approach is easier to compute and is more widely used, since natural textures are made of patterns of irregular subelements. === Edge detection === The use of edge detection is to determine the number of edge pixels in a specified region, helps determine a characteristic of texture complexity. After edges have been found the direction of the edges can also be applied as a characteristic of texture and can be useful in determining patterns in the texture. These directions can be represented as an average or in a histogram. Consider a region with N pixels. the gradient-based edge detector is applied to this region by producing two outputs for each pixel p: the gradient magnitude Mag(p) and the gradient direction Dir(p). The edgeness per unit area can be defined by F e d g e n e s s = | { p | M a g ( p ) > T } | N {\displaystyle F_{edgeness}={\frac {|\{p|Mag(p)>T\}|}{N}}} for some threshold T. To include orientation with edgeness histograms for both gradient magnitude and gradient direction can be used. Hmag(R) denotes the normalized histogram of gradient magnitudes of region R, and Hdir(R) denotes the normalized histogram of gradient orientations of region R. Both are normalized according to the size NR Then F m a g , d i r = ( H m a g ( R ) , H d i r ( R ) ) {\displaystyle F_{mag,dir}=(H_{mag}(R),H_{dir}(R))} is a quantitative texture description of region R. === Co-occurrence matrices === The co-occurrence matrix captures numerical features of a texture using spatial relations of similar gray tones. Numerical features computed from the co-occurrence matrix can be used to represent, compare, and classify textures. The following are a subset of standard features derivable from a normalized co-occurrence matrix: A n g u l a r 2 n d M o m e n t = ∑ i ∑ j p [ i , j ] 2 C o n t r a s t = ∑ i = 1 N g ∑ j = 1 N g n 2 p [ i , j ] , where | i − j | = n C o r r e l a t i o n = ∑ i = 1 N g ∑ j = 1 N g ( i j ) p [ i , j ] − μ x μ y σ x σ y E n t r o p y = − ∑ i ∑ j p [ i , j ] l n ( p [ i , j ] ) {\displaystyle {\begin{aligned}Angular{\text{ }}2nd{\text{ }}Moment&=\sum _{i}\sum _{j}p[i,j]^{2}\\Contrast&=\sum _{i=1}^{Ng}\sum _{j=1}^{Ng}n^{2}p[i,j]{\text{, where }}|i-j|=n\\Correlation&={\frac {\sum _{i=1}^{Ng}\sum _{j=1}^{Ng}(ij)p[i,j]-\mu _{x}\mu _{y}}{\sigma _{x}\sigma _{y}}}\\Entropy&=-\sum _{i}\sum _{j}p[i,j]ln(p[i,j])\\\end{aligned}}} where p [ i , j ] {\displaystyle p[i,j]} is the [ i , j ] {\displaystyle [i,j]} th entry in a gray-tone spatial dependence matrix, and Ng is the number of distinct gray-levels in the quantized image. One negative aspect of the co-occurrence matrix is that the extracted features do not necessarily correspond to visual perception. It is used in dentistry for the objective evaluation of lesions [DOI: 10.1155/2020/8831161], treatment efficacy [DOI: 10.3390/ma13163614; DOI: 10.11607/jomi.5686; DOI: 10.3390/ma13173854; DOI: 10.3390/ma13132935] and bone reconstruction during healing [DOI: 10.5114/aoms.2013.33557; DOI: 10.1259/dmfr/22185098; EID: 2-s2.0-81455161223; DOI: 10.3390/ma13163649]. === Laws texture energy measures === Another approach is to use local masks to detect various types of texture features. Laws originally used four vectors representing texture features to create sixteen 2D masks from the outer products of the pairs of vectors. The four vectors and relevant features were as follows: L5 = [ +1 +4 6 +4 +1 ] (Level) E5 = [ -1 -2 0 +2 +1 ] (Edge) S5 = [ -1 0 2 0 -1 ] (Spot) R5 = [ +1 -4 6 -4 +1 ] (Ripple) To these 4, a fifth is sometimes added: W5 = [ -1 +2 0 -2 +1 ] (Wave) From Laws' 4 vectors, 16 5x5 "energy maps" are then filtered down to 9 in order to remove certain symmetric pairs. For instance, L5E5 measures vertical edge content and E5L5 measures horizontal edge content. The average of these two measures is the "edginess" of the content. The resulting 9 maps used by Laws are as follows: L5E5/E5L5 L5R5/R5L5 E5S5/S5E5 S5S5 R5R5 L5S5/S5L5 E5E5 E5R5/R5E5 S5R5/R5S5 Running each of these nine maps over an image to create a new image of the value of the origin ([2,2]) results in 9 "energy maps," or conceptually an image with each pixel associated with a vector of 9 texture attributes. === Autocorrelation and power spectrum === The autocorrelation function of an image can be used to detect repetitive patterns of textures. == Texture segmentation == The use of image texture can be used as a description for regions into segments. There are two main types of segmentation based on image texture, region based and boundary based. Though image texture is not a perfect measure for segmentation it is used along with other measures, such as color, that helps solve segmenting in image. === Region based === Attempts to group or cluster pixels based on texture properties. === Boundary based === Attempts to group or cluster pixels based on edges between pixels that come from different texture properties.

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  Tabnine

  Tabnine is a code completion tool which uses generative artificial intelligence to assist users by autocompleting code. It was created in 2018 by Jacob Jackson, a student at the University of Waterloo. It is now developed by Tabnine, a software company founded under the name Codota by Dror Weiss and Eran Yahav in Tel Aviv, Israel, in 2013, and renamed to Tabnine in 2021. Initially established under the name Codota, the company underwent a rebranding in May 2021 following the release of the company’s first large language model based AI coding assistant, adopting the name Tabnine. == History == Tabnine was established as Codota in 2013 by Dror Weiss and Eran Yahav in Tel Aviv, Israel. Tabnine, initially founded under the name Codota, was created to develop tools based on over a decade of academic research at the Technion. Codota, the predecessor of Tabnine, secured $2 million in seed investment in June 2017. Following this, in June 2018, the company introduced the first AI-based code completion for Java IDE. In 2019, Codota acquired a product called Tabnine, which used the newly available large-language model technology to provide generative AI for software code across a broader range of programming languages across five IDEs. Codota replaced its earlier approach to code generation with this new approach to generative AI. The company secured a Series A round of funding in April 2020, raising $12 million. On May 26, 2021, Codota changed its name to Tabnine and underwent a corresponding rebranding. By April 2022, Tabnine reached over one million users. In June of the same year, Tabnine launched models that could predict full lines and snippets of code. The same year it raised $15.5 mln in a funding round led co-led by Qualcomm Ventures. In June 2023, Tabnine introduced an AI-powered chat agent, enabling developers to use natural language to generate code, to explain code, to generate tests and documentation, and to propose fixes to code. In November 2023, Tabnine closed a Series B round of funding, raising $25 million to scale the company’s operations. == Operations == Tabnine's headquarters is located in Tel Aviv, Israel, with an additional corporate entity in the United States. As of November 2023, Tabnine generative AI for software development is used by a million developers. It has 10 million installations across VS Code and JetBrains. Since its founding, Dror Weiss has served as CEO, with Eran Yahav as CTO.

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  Brain.js

  Brain.js is a JavaScript library used for neural networking, which is released as free and open-source software under the MIT License. It can be used in both the browser and Node.js backends. Brain.js is most commonly used as a simple introduction to neural networking, as it hides complex mathematics and has a familiar modern JavaScript syntax. It is maintained by members of the Brain.js organization and open-source contributors. == Examples == Creating a feedforward neural network with backpropagation: Creating a recurrent neural network: Train the neural network on RGB color contrast:

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